Overview

Dataset statistics

Number of variables9
Number of observations1030
Missing cells0
Missing cells (%)0.0%
Duplicate rows11
Duplicate rows (%)1.1%
Total size in memory72.5 KiB
Average record size in memory72.1 B

Variable types

Numeric9

Alerts

Dataset has 11 (1.1%) duplicate rowsDuplicates
Age (day) is highly overall correlated with concrete_strengthHigh correlation
Superplasticizer is highly overall correlated with WaterHigh correlation
Water is highly overall correlated with SuperplasticizerHigh correlation
concrete_strength is highly overall correlated with Age (day)High correlation

Reproduction

Analysis started2023-12-29 11:19:54.060443
Analysis finished2023-12-29 11:20:35.176672
Duration41.12 seconds
Software versionydata-profiling vv4.6.3
Download configurationconfig.json

Variables

Cement
Real number (ℝ)

Distinct278
Distinct (%)27.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean281.16786
Minimum102
Maximum540
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2023-12-29T16:50:35.752313image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum102
5-th percentile143.745
Q1192.375
median272.9
Q3350
95-th percentile480
Maximum540
Range438
Interquartile range (IQR)157.625

Descriptive statistics

Standard deviation104.50636
Coefficient of variation (CV)0.37168673
Kurtosis-0.52065228
Mean281.16786
Median Absolute Deviation (MAD)79.4
Skewness0.50948118
Sum289602.9
Variance10921.58
MonotonicityNot monotonic
2023-12-29T16:50:36.557686image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
362.6 20
 
1.9%
425 20
 
1.9%
251.4 15
 
1.5%
310 14
 
1.4%
446 14
 
1.4%
331 13
 
1.3%
475 13
 
1.3%
250 13
 
1.3%
349 12
 
1.2%
387 12
 
1.2%
Other values (268) 884
85.8%
ValueCountFrequency (%)
102 4
0.4%
108.3 4
0.4%
116 4
0.4%
122.6 4
0.4%
132 2
 
0.2%
133 5
0.5%
133.1 1
 
0.1%
134.7 1
 
0.1%
135 2
 
0.2%
135.7 2
 
0.2%
ValueCountFrequency (%)
540 9
0.9%
531.3 5
0.5%
528 1
 
0.1%
525 7
0.7%
522 2
 
0.2%
520 2
 
0.2%
516 2
 
0.2%
505 1
 
0.1%
500.1 1
 
0.1%
500 10
1.0%

Blast_furn_slag
Real number (ℝ)

Distinct185
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.353107
Minimum1
Maximum359.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2023-12-29T16:50:37.510156image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median22
Q3142.95
95-th percentile236
Maximum359.4
Range358.4
Interquartile range (IQR)141.95

Descriptive statistics

Standard deviation85.887864
Coefficient of variation (CV)1.1551348
Kurtosis-0.49308665
Mean74.353107
Median Absolute Deviation (MAD)21
Skewness0.80741053
Sum76583.7
Variance7376.7252
MonotonicityNot monotonic
2023-12-29T16:50:38.451880image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 471
45.7%
189 30
 
2.9%
106.3 20
 
1.9%
24 14
 
1.4%
20 12
 
1.2%
145 11
 
1.1%
98.1 10
 
1.0%
19 10
 
1.0%
26 8
 
0.8%
22 8
 
0.8%
Other values (175) 436
42.3%
ValueCountFrequency (%)
1 471
45.7%
11 4
 
0.4%
13.6 5
 
0.5%
15 5
 
0.5%
17.2 1
 
0.1%
17.5 1
 
0.1%
17.6 1
 
0.1%
19 10
 
1.0%
20 12
 
1.2%
22 8
 
0.8%
ValueCountFrequency (%)
359.4 2
 
0.2%
342.1 2
 
0.2%
316.1 2
 
0.2%
305.3 4
0.4%
290.2 2
 
0.2%
288 4
0.4%
282.8 4
0.4%
272.8 2
 
0.2%
262.2 5
0.5%
260 1
 
0.1%

Fly_Ash
Real number (ℝ)

Distinct156
Distinct (%)15.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.737864
Minimum1
Maximum200.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2023-12-29T16:50:39.049355image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q3118.3
95-th percentile167
Maximum200.1
Range199.1
Interquartile range (IQR)117.3

Descriptive statistics

Standard deviation63.531503
Coefficient of variation (CV)1.16065
Kurtosis-1.319259
Mean54.737864
Median Absolute Deviation (MAD)0
Skewness0.5418382
Sum56380
Variance4036.2519
MonotonicityNot monotonic
2023-12-29T16:50:40.342613image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 566
55.0%
118.3 20
 
1.9%
141 16
 
1.6%
24.5 15
 
1.5%
79 14
 
1.4%
94 13
 
1.3%
100.4 11
 
1.1%
125.2 10
 
1.0%
95.7 10
 
1.0%
98.8 10
 
1.0%
Other values (146) 345
33.5%
ValueCountFrequency (%)
1 566
55.0%
24.5 15
 
1.5%
59 1
 
0.1%
60 1
 
0.1%
71 1
 
0.1%
71.5 1
 
0.1%
75.6 1
 
0.1%
76 1
 
0.1%
77 2
 
0.2%
78 2
 
0.2%
ValueCountFrequency (%)
200.1 1
 
0.1%
200 1
 
0.1%
195 3
0.3%
194.9 1
 
0.1%
194 1
 
0.1%
193 1
 
0.1%
190 1
 
0.1%
187 1
 
0.1%
185.3 1
 
0.1%
185 2
0.2%

Water
Real number (ℝ)

HIGH CORRELATION 

Distinct195
Distinct (%)18.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean181.56728
Minimum121.8
Maximum247
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2023-12-29T16:50:41.408528image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum121.8
5-th percentile146.1
Q1164.9
median185
Q3192
95-th percentile228
Maximum247
Range125.2
Interquartile range (IQR)27.1

Descriptive statistics

Standard deviation21.354219
Coefficient of variation (CV)0.1176105
Kurtosis0.12208167
Mean181.56728
Median Absolute Deviation (MAD)13
Skewness0.074628384
Sum187014.3
Variance456.00265
MonotonicityNot monotonic
2023-12-29T16:50:42.444961image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
192 118
 
11.5%
228 54
 
5.2%
185.7 46
 
4.5%
203.5 36
 
3.5%
186 28
 
2.7%
164.9 20
 
1.9%
162 20
 
1.9%
185 15
 
1.5%
153.5 15
 
1.5%
200 14
 
1.4%
Other values (185) 664
64.5%
ValueCountFrequency (%)
121.8 5
0.5%
126.6 5
0.5%
127 1
 
0.1%
127.3 1
 
0.1%
137.8 5
0.5%
140 1
 
0.1%
140.8 5
0.5%
141.8 5
0.5%
142 1
 
0.1%
143.3 5
0.5%
ValueCountFrequency (%)
247 1
 
0.1%
246.9 1
 
0.1%
237 1
 
0.1%
236.7 1
 
0.1%
228 54
5.2%
221.4 1
 
0.1%
221 2
 
0.2%
220.1 1
 
0.1%
220 2
 
0.2%
219.7 1
 
0.1%

Superplasticizer
Real number (ℝ)

HIGH CORRELATION 

Distinct111
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5726214
Minimum1
Maximum32.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2023-12-29T16:50:43.355118image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median6.4
Q310.2
95-th percentile16.055
Maximum32.2
Range31.2
Interquartile range (IQR)9.2

Descriptive statistics

Standard deviation5.5990166
Coefficient of variation (CV)0.85186965
Kurtosis2.1180526
Mean6.5726214
Median Absolute Deviation (MAD)5.3
Skewness1.1098343
Sum6769.8
Variance31.348987
MonotonicityNot monotonic
2023-12-29T16:50:44.326641image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 379
36.8%
11.6 37
 
3.6%
8 27
 
2.6%
7 19
 
1.8%
6 17
 
1.7%
9.9 16
 
1.6%
8.9 16
 
1.6%
7.8 16
 
1.6%
9 16
 
1.6%
10 15
 
1.5%
Other values (101) 472
45.8%
ValueCountFrequency (%)
1 379
36.8%
1.7 4
 
0.4%
1.9 1
 
0.1%
2 1
 
0.1%
2.2 1
 
0.1%
2.5 2
 
0.2%
3 6
 
0.6%
3.1 1
 
0.1%
3.4 3
 
0.3%
3.6 5
 
0.5%
ValueCountFrequency (%)
32.2 5
0.5%
28.2 5
0.5%
23.4 5
0.5%
22.1 1
 
0.1%
22 6
0.6%
20.8 1
 
0.1%
20 1
 
0.1%
19 1
 
0.1%
18.8 1
 
0.1%
18.6 5
0.5%

Coarse_Agg
Real number (ℝ)

Distinct284
Distinct (%)27.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean972.91893
Minimum801
Maximum1145
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2023-12-29T16:50:45.335132image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum801
5-th percentile842
Q1932
median968
Q31029.4
95-th percentile1104
Maximum1145
Range344
Interquartile range (IQR)97.4

Descriptive statistics

Standard deviation77.753954
Coefficient of variation (CV)0.079918225
Kurtosis-0.5990161
Mean972.91893
Median Absolute Deviation (MAD)46.3
Skewness-0.040219745
Sum1002106.5
Variance6045.6774
MonotonicityNot monotonic
2023-12-29T16:50:46.595572image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
932 57
 
5.5%
852.1 45
 
4.4%
944.7 30
 
2.9%
968 29
 
2.8%
1125 24
 
2.3%
1047 19
 
1.8%
967 19
 
1.8%
974 12
 
1.2%
942 12
 
1.2%
938 12
 
1.2%
Other values (274) 771
74.9%
ValueCountFrequency (%)
801 4
0.4%
801.1 1
 
0.1%
801.4 1
 
0.1%
811 2
0.2%
814 1
 
0.1%
814.1 1
 
0.1%
817.9 1
 
0.1%
818 1
 
0.1%
819 2
0.2%
819.2 1
 
0.1%
ValueCountFrequency (%)
1145 1
 
0.1%
1134.3 5
 
0.5%
1130 1
 
0.1%
1125 24
2.3%
1124.4 2
 
0.2%
1120 2
 
0.2%
1119 2
 
0.2%
1118.8 2
 
0.2%
1118 1
 
0.1%
1113 2
 
0.2%

fine_Agg
Real number (ℝ)

Distinct302
Distinct (%)29.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean773.58049
Minimum594
Maximum992.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2023-12-29T16:50:47.424481image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum594
5-th percentile613
Q1730.95
median779.5
Q3824
95-th percentile898.09
Maximum992.6
Range398.6
Interquartile range (IQR)93.05

Descriptive statistics

Standard deviation80.17598
Coefficient of variation (CV)0.10364271
Kurtosis-0.10217699
Mean773.58049
Median Absolute Deviation (MAD)45.5
Skewness-0.2530096
Sum796787.9
Variance6428.1878
MonotonicityNot monotonic
2023-12-29T16:50:48.316853image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
755.8 30
 
2.9%
594 30
 
2.9%
670 23
 
2.2%
613 22
 
2.1%
801 16
 
1.6%
746.6 15
 
1.5%
887.1 15
 
1.5%
712 14
 
1.4%
845 14
 
1.4%
750 12
 
1.2%
Other values (292) 839
81.5%
ValueCountFrequency (%)
594 30
2.9%
605 5
 
0.5%
611.8 5
 
0.5%
612 1
 
0.1%
613 22
2.1%
613.2 2
 
0.2%
614 1
 
0.1%
623 2
 
0.2%
630 5
 
0.5%
631 4
 
0.4%
ValueCountFrequency (%)
992.6 5
0.5%
945 4
0.4%
943.1 4
0.4%
942 4
0.4%
925.7 5
0.5%
905.9 5
0.5%
903.8 5
0.5%
903.6 5
0.5%
901.8 5
0.5%
900.9 5
0.5%

Age (day)
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.662136
Minimum1
Maximum365
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2023-12-29T16:50:48.984636image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q17
median28
Q356
95-th percentile180
Maximum365
Range364
Interquartile range (IQR)49

Descriptive statistics

Standard deviation63.169912
Coefficient of variation (CV)1.38342
Kurtosis12.168989
Mean45.662136
Median Absolute Deviation (MAD)21
Skewness3.2691774
Sum47032
Variance3990.4377
MonotonicityNot monotonic
2023-12-29T16:50:49.932162image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
28 425
41.3%
3 134
 
13.0%
7 126
 
12.2%
56 91
 
8.8%
14 62
 
6.0%
90 54
 
5.2%
100 52
 
5.0%
180 26
 
2.5%
91 22
 
2.1%
365 14
 
1.4%
Other values (4) 24
 
2.3%
ValueCountFrequency (%)
1 2
 
0.2%
3 134
 
13.0%
7 126
 
12.2%
14 62
 
6.0%
28 425
41.3%
56 91
 
8.8%
90 54
 
5.2%
91 22
 
2.1%
100 52
 
5.0%
120 3
 
0.3%
ValueCountFrequency (%)
365 14
 
1.4%
360 6
 
0.6%
270 13
 
1.3%
180 26
 
2.5%
120 3
 
0.3%
100 52
 
5.0%
91 22
 
2.1%
90 54
 
5.2%
56 91
 
8.8%
28 425
41.3%

concrete_strength
Real number (ℝ)

HIGH CORRELATION 

Distinct845
Distinct (%)82.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.817961
Minimum2.33
Maximum82.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2023-12-29T16:50:51.100880image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2.33
5-th percentile10.961
Q123.71
median34.445
Q346.135
95-th percentile66.802
Maximum82.6
Range80.27
Interquartile range (IQR)22.425

Descriptive statistics

Standard deviation16.705742
Coefficient of variation (CV)0.46640684
Kurtosis-0.31372486
Mean35.817961
Median Absolute Deviation (MAD)10.93
Skewness0.41697729
Sum36892.5
Variance279.08181
MonotonicityNot monotonic
2023-12-29T16:50:51.952119image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.4 6
 
0.6%
77.3 4
 
0.4%
79.3 4
 
0.4%
31.35 4
 
0.4%
71.3 4
 
0.4%
35.3 4
 
0.4%
23.52 4
 
0.4%
41.05 4
 
0.4%
44.28 3
 
0.3%
41.54 3
 
0.3%
Other values (835) 990
96.1%
ValueCountFrequency (%)
2.33 1
0.1%
3.32 1
0.1%
4.57 1
0.1%
4.78 1
0.1%
4.83 1
0.1%
4.9 1
0.1%
6.27 1
0.1%
6.28 1
0.1%
6.47 1
0.1%
6.81 1
0.1%
ValueCountFrequency (%)
82.6 1
 
0.1%
81.75 1
 
0.1%
80.2 1
 
0.1%
79.99 1
 
0.1%
79.4 1
 
0.1%
79.3 4
0.4%
78.8 1
 
0.1%
77.3 4
0.4%
76.8 1
 
0.1%
76.24 1
 
0.1%

Interactions

2023-12-29T16:50:27.725309image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:49:54.566687image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:00.204320image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:05.429624image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:09.371676image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:12.670900image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:15.813866image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:19.025632image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:22.314347image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:28.444885image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:49:55.593433image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:00.926785image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:05.824876image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:09.682445image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:13.016587image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:16.132908image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:19.355332image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:22.640421image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:29.178610image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:49:56.400723image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:01.677732image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:06.685393image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:10.029684image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:13.362647image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:16.485897image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:19.730166image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:22.997104image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:29.777923image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:49:57.036542image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:02.183604image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:07.179178image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:10.378407image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:13.725621image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:16.864910image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:20.111874image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:23.628438image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:30.372433image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:49:57.397015image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:02.615321image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:07.572943image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:10.951100image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:14.061174image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:17.212846image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:20.465934image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:24.233665image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:31.147641image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:49:58.247450image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:03.150969image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:07.912906image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:11.261868image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:14.380708image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:17.561438image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:20.841722image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:25.404261image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:31.897597image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:49:58.665587image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:03.725596image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:08.274362image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:11.615855image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:14.742037image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:17.923007image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:21.200016image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:26.009064image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:32.496853image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:49:59.085103image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:04.547980image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:08.650269image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:11.962604image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:15.104873image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:18.289948image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:21.562980image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:26.882724image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:33.185438image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:49:59.583170image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:04.975855image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:08.997920image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:12.311051image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:15.444433image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:18.657836image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:21.936673image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T16:50:27.280691image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2023-12-29T16:50:52.417519image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Age (day)Blast_furn_slagCementCoarse_AggFly_AshSuperplasticizerWaterconcrete_strengthfine_Agg
Age (day)1.000-0.0180.005-0.0450.003-0.0100.0910.596-0.057
Blast_furn_slag-0.0181.000-0.245-0.349-0.2540.0980.0530.164-0.302
Cement0.005-0.2451.000-0.145-0.4180.038-0.0940.478-0.174
Coarse_Agg-0.045-0.349-0.1451.0000.058-0.199-0.218-0.184-0.100
Fly_Ash0.003-0.254-0.4180.0581.0000.454-0.283-0.0780.051
Superplasticizer-0.0100.0980.038-0.1990.4541.000-0.6870.3480.168
Water0.0910.053-0.094-0.218-0.283-0.6871.000-0.308-0.346
concrete_strength0.5960.1640.478-0.184-0.0780.348-0.3081.000-0.180
fine_Agg-0.057-0.302-0.174-0.1000.0510.168-0.346-0.1801.000

Missing values

2023-12-29T16:50:34.040476image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-29T16:50:34.942071image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CementBlast_furn_slagFly_AshWaterSuperplasticizerCoarse_Aggfine_AggAge (day)concrete_strength
0540.01.01.0162.02.51040.0676.02879.99
1540.01.01.0162.02.51055.0676.02861.89
2332.5142.51.0228.01.0932.0594.027040.27
3332.5142.51.0228.01.0932.0594.036541.05
4198.6132.41.0192.01.0978.4825.536044.30
5266.0114.01.0228.01.0932.0670.09047.03
6380.095.01.0228.01.0932.0594.036543.70
7380.095.01.0228.01.0932.0594.02836.45
8266.0114.01.0228.01.0932.0670.02845.85
9475.01.01.0228.01.0932.0594.02839.29
CementBlast_furn_slagFly_AshWaterSuperplasticizerCoarse_Aggfine_AggAge (day)concrete_strength
1020288.4121.01.0177.47.0907.9829.52842.14
1021298.21.0107.0209.711.1879.6744.22831.88
1022264.5111.086.5195.55.9832.6790.42841.54
1023159.8250.01.0168.412.21049.3688.22839.46
1024166.0259.71.0183.212.7858.8826.82837.92
1025276.4116.090.3179.68.9870.1768.32844.28
1026322.21.0115.6196.010.4817.9813.42831.18
1027148.5139.4108.6192.76.1892.4780.02823.70
1028159.1186.71.0175.611.3989.6788.92832.77
1029260.9100.578.3200.68.6864.5761.52832.40

Duplicate rows

Most frequently occurring

CementBlast_furn_slagFly_AshWaterSuperplasticizerCoarse_Aggfine_AggAge (day)concrete_strength# duplicates
1362.6189.01.0164.911.6944.7755.8335.304
3362.6189.01.0164.911.6944.7755.82871.304
4362.6189.01.0164.911.6944.7755.85677.304
5362.6189.01.0164.911.6944.7755.89179.304
2362.6189.01.0164.911.6944.7755.8755.903
6425.0106.31.0153.516.5852.1887.1333.403
7425.0106.31.0153.516.5852.1887.1749.203
8425.0106.31.0153.516.5852.1887.12860.293
9425.0106.31.0153.516.5852.1887.15664.303
10425.0106.31.0153.516.5852.1887.19165.203